Rapport-building: Chat versus in-person witness interviews

Tactics recommended for rapport-building consist of verbal (e.g., finding common ground or shared experiences) and non-verbal (e.g., affirmations, displaying empathy) behaviours. Most of the research on rapport, however, has examined it in in-person contexts, where both verbal and non-verbal behaviours are present. In this study, we were interested in the effectiveness of rapport-building when conducting online witness interviews via chat, which de-emphasises the use non-verbal rapport behaviours, compared to traditional in-person interviews. Participants (N = 131) experienced a virtual reality (VR) scenario depicting a mock crime and were interviewed either in person or online via the chat function in Skype. Participants perceived rapport more positively when interviewed in person for three measures: attentiveness, trust and respect and expertise. Two other measures, cultural similarity and connected flow, were not perceived differently across interview medium. Participants interviewed online via chat disclosed similar amounts of crime-related information and were just as accurate as participants interviewed in person. We found that in-person interviews yielded better rapport ratings than interviews via chat but were equally productive in terms of the quality of information obtained, as measured by crime-related details and accuracy. If witnesses are to be interviewed via chat, investigators must carefully consider how to compensate for the lack of those non-verbal rapport tactics that influence witnesses' perceptions of attentiveness, trust/respect and investigator's expertise.     

Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets

Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric, implying linear relationships between the variables at hand; alternatively, non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components (PCs) is maximized. We propose a penalized version of non-linear PCA for ordinal variables that is a smoothed intermediate between standard PCA on category labels and non-linear PCA as used so far. The new approach is by no means limited to monotonic effects and offers both better interpretability of the non-linear transformation of the category labels and better performance on validation data than unpenalized non-linear PCA and/or standard linear PCA. In particular, an application of penalized optimal scaling to ordinal data as given with the International Classification of Functioning, Disability and Health (ICF) is provided.     

Lifespan normative data (18–89 years) for Raven's Advanced Progressive Matrices Set I

Raven's Advanced Progressive Matrices (APM) Set I is a validated and brief test of fluid intelligence, ideal for use in busy clinical settings. However, there is a dearth of normative data allowing an accurate interpretation of APM scores. To address this, we present normative data from across the adult lifespan (18–89 years) for the APM Set I. Data are presented in five age cohorts (total N = 352), including two older adult cohorts (65–79 years and 80–89 years), which allows age-standardized assessment. We also present data from a validated measure of premorbid intellectual ability, which was absent from previous standardizations of longer forms of the APM. In line with previous findings, a striking age-related decline was noted, beginning relatively early in adulthood and most marked amongst lower-scoring individuals. Older adults did not demonstrate difficulty with specific test items or make an increased proportion of specific errors. Sex was not a significant predictor of performance. The data set is of particular use in the neuropsychological assessment of older adults, given the known susceptibility of fluid intelligence to both the effects of normal ageing and acquired brain injury in older age. The results are discussed in light of theories of neurological ageing.     

Data Smoothing: Prediction of Human Behavior, Detection of Behavioral Patterns, and Monitoring Treatment Effectiveness in Single-Subject Behavioral Studies

Data-smoothing can be particularly useful in predicting human behavior, detecting behavioral patterns, and monitoring treatment effectiveness in highly variable single-subject behavioral experiments that cannot be determined by only visual inspection of their graphs. Using an example from the applied behavior analytic literature, the use of moving-average and exponential data-smoothing aided the detection of the unique behavioral patterns of a child with autism across different treatments. Furthermore, the utility of the data-smoothing procedures to monitor and control the effectiveness of an intervention is discussed. The ease of their calculations suggest use of data-smoothing by behavior analysts whenever the effects of particular interventions are questionable.     

A marginalization model for the multidimensional unfolding analysis of ranking data

A marginalization model for the multidimensional unfolding analysis of ranking data is presented. A subject samples one of a number of random points that are multivariate normally distributed. The subject perceives the distances from the point to all the stimulus points fixed in the same multidimensional space. The distances are error perturbed in this perception process. He/she produces a ranking dependent on these error-perturbed distances. The marginal probability of a ranking is obtained according to this ranking model and by integrating out the subject (ideal point) parameters, assuming the above distribution. One advantage of the model is that the individual differences are captured using the posterior probabilities of subject points. Three sets of ranking data are analyzed by the model.     

Guidelines for training in the ethical conduct of scientific research

Historically, scientists in training have learned the rules of ethical conduct by the example of their advisors and other senior scientists and by practice. This paper is intended to serve as a guide for the beginning scientist to some fundamental principles of scientific research ethics. The paper focuses less on issues of outright dishonesty or fraud, and more on the positive aspects of ethical scientific behavior; in other words, what a scientist should do to maintain a high level of ethical conduct in research. There are a number of fairly specific rules, guidelines, or commonly accepted operating principles that have evolved for the ethical conduct of science. In order to discuss this code of ethics, this paper is divided into sections dealing with specific areas of scientific ethics. These areas are: data collection and storage, ownership of data, confidentiality, communication, authorship, collaboration, the peer review system, and rules of dealing with ethical complaints. Illustrative case histories are presented to provide examples of the type of ethical dispute or problem being discussed. If scientific trainees learn the accepted rules of behavior that govern the conduct of science, ethical problems that arise out of ignorance, misunderstanding, or poor communication can be avoided.     

Tests of symmetry in three-way contingency tables

Three-dimensional contingency tables are analyzed, with one variable (e.g., sex) as a factor, and with a natural relation between the other variables (e.g. left and right eye vision). Models of special interest, like symmetry and proportional symmetry between the related variables, and homogeneity across the factor levels, are investigated. Maximum likelihood estimators of parameters and partitions of chi-square goodness-of-fit statistics are explicitly presented; the independence of certain models is noted, and an example is discussed.     

Seat belt usage: A potential target for applied behavior analysis

Results of 1,579 observations of cars entering or exiting campus parking lots showed direct relationships between seat belt wearing and the intrusiveness of the engineering device designed to induce belt usage, and between device intrusiveness and system defeat. For example, all drivers with working interlocks or unlimited buzzer reminders were wearing a seat belt; but 62% of the systems with interlocks or unlimited buzzers had been defeated, and only 15.9% of the drivers in these cars were wearing a seat belt. The normative data indicated marked ineffectiveness of the negative reinforcement contingencies implied by current seat belt inducement systems; but suggested that unlimited buzzer systems would be the optimal system currently available if contingencies were developed to discourage the disconnection and circumvention of such systems. Positive reinforcement strategies are discussed that would be quite feasible for large-scale promotion of seat belt usage.     

A bayesian approach to confirmatory factor analysis

Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information on parameter is incorporated in the analysis. An iterative algorithm is developed to obtain the Bayes estimates. A numerical example based on longitudinal data is presented. A simulation study is designed to compare the Bayesian approach with the maximum likelihood method.Computer facilities were provided by the Computer Services Center, The Chinese University of Hong Kong.     

Maximum likelihood estimation of the joint covariance matrix for sections of tests given to distinct samples with application to test equating

Consider an old testX consisting ofs sections and two new testsY andZ similar toX consisting ofp andq sections respectively. All subjects are given testX plus two variable sections from either testY orZ. Different pairings of variable sections are given to each subsample of subjects. We present a method of estimating the covariance matrix of the combined test (X ₁, ...,X _s ,Y ₁, ...,Y _p ,Z ₁, ...,Z _q ) and describe an application of these estimation techniques to linear, observed-score, test equating.The author is indebted to Paul W. Holland and Donald B. Rubin for their encouragement and many helpful comments and suggestions that contributed significantly to the development of this paper.This research was supported by the Program Statistics Research Project of the ETS Research Statistics Group.